The Auckland University of Technology is a university in New Zealand. It was formed on 1 January 2000 when the Auckland Institute of Technology was granted university status. Its primary campus is on Wellesley Street in Auckland's Central business district . AUT has three secondary campuses: North Shore, South, and the Millennium Institute of Sport and Health . For branding purposes, since 2010 the Auckland University of Technology refers to itself as AUT University. Wikipedia.
News Article | April 21, 2017
Singapore is aiming to be the world's first Smart Nation - but what does that actually entail? You know what it's like. You're waiting for the bus on your way to work and inevitably, you're late. Enter Singapore's Smart Nation solution, which aims to merge technology into every aspect of life on the small island. That includes some bus stops, which under this plan will now have interactive maps and wi-fi connectivity - even e-books and a swing. This is all an attempt to make the journeys of Singapore's commuters more enjoyable and efficient. If you look at how important the bus system is to public transport here, it makes sense. With almost four million daily rides, the bus network makes up the most significant part of Singapore's transport network. Nowhere is the scale of the project more evident than at the headquarters of the Land Transport Authority. Using GPS data, researchers and programmers can tell how fast or slow a bus is going and how many people are on board at any given time. "With this information we know where are the choke points at different times of the day," said Christopher Hooi Wai Yean, deputy director of the authority's communications and sensors division, as he demonstrated the movement of the buses on their screens. "[This way] we can put in measures to alleviate and dissipate the crowd at choke points across the island. This will ensure that the whole transport system is more well-oiled in that sense." It is an approach that is being replicated across all sectors - transport, homes, offices and even hospitals. The KK Women's and Children's Hospital is one of the biggest and busiest in Singapore. On any day, it sees scores of patients - mainly pregnant women or mums with their kids. It began trialling video conferencing for its patients in non-emergency cases in November last year. Gladys Soo is one such mum. Her seven-year old son suffers from eczema and she started treatment for him in February. "We went to the hospital in person for the first consultation to check for his eczema," she told me. "The follow-up was done via video conferencing." Mrs Soo said the fact that she is a working mum was a factor in her decision to go for the video-conferencing option. "It saves you time - I don't have to travel, I don't have to take leave. The pharmacist can actually view my son's eczema on the video conference. And he can diagnose whether it is getting better - it is like being with him in person." Speech therapy, lactation consultation services and paediatric home care services are other aspects of medical care that KKH is using video conferencing to address. Associate Prof Low Cheng Ooi, the chief clinical informatics officer of IHiS, the company that manages the technology infrastructure for the healthcare aspect of Singapore's Smart Nation solutions, says the plan is to phase this programme in gradually. "We already know that public health services delivered over video and medical consultation via video works well in larger countries with rural areas," he told me. "But in Singapore we are very urban, and our citizens can get healthcare within a very short period of time. "So we have to rationalise what it is we are trying to do with this platform. We are moving cautiously with discipline so that patients will benefit from this kind of consultation, with no risks." It's an ambitious goal - trying to merge technology into every aspect of citizens' lives - but this grand plan may have already run into some speed bumps. "We really are not going as fast as we ought to," said the country's Prime Minister Lee Hsien Loong recently. To deliver results, Singapore has set up a new ministerial committee to push ahead with its Smart Nation dreams. Vivian Balakrishnan, the minister in charge of the Smart Nation initiative, says that a sense of urgency is vital in ensuring the future success of Singapore. "If we don't get this right, jobs are at stake," he told the BBC. "Wages are at stake and any government that doesn't prepare its people for the future and offer the potential for good jobs will be in trouble." But Harminder Singh, a senior lecturer in business information systems at the Auckland University of Technology in New Zealand, says the main issue with Smart Nation is that there may be too much government control over it right now for real innovation to take place. The future of a good night out Are our cities killing us with bad air? Will we travel to work in jetpacks? "Singapore's way of doing things is that the government leads, then others follow," he told me. "This might be a problem - it is too centralised and so it may take too long for plans to trickle down. "And ideas from the ground may be neither visible to those on top nor acceptable to them, especially if they are related to the delivery of services that are traditionally handled by the government." He adds that it is not clear why Singapore's leaders are so keen to move full steam ahead with this plan. "Smart Nation is about building national technology infrastructure so that the government can offer new services, or do what they do now differently. The government may need to explain more clearly how the Smart Nation project will improve salaries and jobs in Singapore to get the project moving faster." The authorities here are taking this initiative extremely seriously - it appears to be the big bet Singapore is taking for its next generation. Because of its size, Singapore has always had to stay one step ahead of the curve to survive. This tiny island nation has always prided itself on persistence and a strong work ethic to succeed. Smart Nation is its plan for its future survival - and the shoots of innovation are beginning to show. Singapore now has a small but growing start-up culture and home-grown companies are starting to take more risks. But creativity needs to be nurtured - and Singapore may find that its Smart Nation dreams may take time to reach their full potential.
Agency: European Commission | Branch: FP7 | Program: CP-IP | Phase: HEALTH.2013.2.2.1-1 | Award Amount: 39.56M | Year: 2013
Traumatic Brain Injury (TBI) is a major cause of death and disability, leading to great personal suffering to victim and relatives, as well as huge direct and indirect costs to society. Strong ethical, medical, social and health economic reasons therefore exist for improving treatment. The CENTER-TBI project will collect a prospective, contemporary, highly granular, observational dataset of 5400 patients, which will be used for better characterization of TBI and for Comparative Effectiveness Research (CER). The generalisability of our results will be reinforced by a contemporaneous registry level data collection in 15-25,000 patients. Our conceptual approach is to exploit the heterogeneity in biology, care, and outcome of TBI, to discover novel pathophysiology, refine disease characterization, and identify effective clinical interventions. Key elements are the use of emerging technologies (biomarkers, genomics and advanced MR imaging) in large numbers of patients, across the entire course of TBI (from injury to late outcome) and across all severities of injury (mild to severe). Improved characterization with these tools will aid Precision Medicine, a concept recently advocated by the US National Academy of Science, facilitating targeted management for individual patients. Our consortium includes leading experts and will bring outstanding biostatistical and neuroinformatics expertise to the project. Collaborations with external partners, other FP7 consortia, and international links within InTBIR, will greatly augment scientific resources and broaden the global scope of our research. We anticipate that the project could revolutionize our view of TBI, leading to more effective and efficient therapy, thus improving outcome and reducing costs. These outcomes reflect the goals of CER to assist consumers, clinicians, health care purchasers, and policy makers to make informed decisions, and will improve healthcare at both individual and population levels.
Ikeda E.,Auckland University of Technology
Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation | Year: 2014
To review the use of quality of life (QOL) measures utilised in children and youth with autism spectrum disorder (ASD). Relevant articles were identified through database searches using MEDLINE, CINAHL Plus with Full Text and SPORTDiscus with Full Text via EBSCO Health Database, PsycINFO and ProQuest Health and Medicine (from 2000 to May 2013). Original research articles were included that measured QOL in children and youth with ASD aged 5-20 years. Searches were limited to articles from peer-reviewed journals, in English or German, and those available in full text. The search identified 1,165 titles and 13 met the inclusion criteria. The review identified a number of QOL measures used in children and youth with ASD, with the most common one being the Pediatric Quality of Life Inventory™ (PedsQL). QOL measures using self-reports were uncommon, and the reliability and validity of QOL measures were not sufficiently reported for this population. Large discrepancies in QOL scores were found between self-reports and proxy-reports. Despite the differences in study design and methodological quality, there was consistency in the results among studies; children and youth with ASD provided lower QOL scores, particularly for social domains, compared to their healthy counterparts. The PedsQL is likely to be an appropriate QOL measure for use in children and youth with ASD. Future research should focus on examining the appropriateness, reliability and validity of QOL self-reports for use in this population.
Kasabov N.,Auckland University of Technology
Neural Networks | Year: 2010
Spiking neural networks (SNN) are promising artificial neural network (ANN) models as they utilise information representation as trains of spikes, that adds new dimensions of time, frequency and phase to the structure and the functionality of ANN. The current SNN models though are deterministic, that restricts their applications for large scale engineering and cognitive modelling of stochastic processes. This paper proposes a novel probabilistic spiking neuron model (pSNM) and suggests ways of building pSNN for a wide range of applications including classification, string pattern recognition and associative memory. It also extends previously published computational neurogenetic models. © 2009 Elsevier Ltd. All rights reserved.
Cowpertwait P.S.P.,Auckland University of Technology
Water Resources Research | Year: 2010
A point process rainfall model is further developed that has storm origins occurring in space-time according to a Poisson process, where each storm origin has a random radius so that storms occur as circular regions in two-dimensional space, where the storm radii are taken to be independent exponential random variables. Each storm origin is of random type z, where z follows a continuous probability distribution. Cell origins occur in a further spatial Poisson process and have arrival times that follow a Neyman-Scott point process. Each cell origin has a radius so that cells form discs in two-dimensional space, where the cell radii are independent exponential random variables. Each cell has a random lifetime and an intensity that remains constant over both the cell lifetime and cell disk area. Statistical properties up to third order are given for the model. Using these properties, the model is fitted to 10 min series taken from 23 sites across the Rome region, Italy. Distributional properties of the observed annual maxima are compared to equivalent values sampled from series that are simulated using the fitted model. The results indicate that the model will be of use in urban drainage projects for the Rome region. Copyright 2010 by the American Geophysical Union.
Hankin R.K.S.,Auckland University of Technology
Journal of Statistical Software | Year: 2012
A multivariate generalization of the emulator technique described by Hankin (2005) is presented in which random multivariate functions may be assessed. In the standard univariate case (Oakley 1999), a Gaussian process, a finite number of observations is made; here, observations of different types are considered. The technique has the property that marginal analysis (that is, considering only a single observation type) reduces exactly to the univariate theory. The associated software is used to analyze datasets from the field of climate change.
Kasabov N.K.,Auckland University of Technology
Neural Networks | Year: 2014
The brain functions as a spatio-temporal information processing machine. Spatio- and spectro-temporal brain data (STBD) are the most commonly collected data for measuring brain response to external stimuli. An enormous amount of such data has been already collected, including brain structural and functional data under different conditions, molecular and genetic data, in an attempt to make a progress in medicine, health, cognitive science, engineering, education, neuro-economics, Brain-Computer Interfaces (BCI), and games. Yet, there is no unifying computational framework to deal with all these types of data in order to better understand this data and the processes that generated it. Standard machine learning techniques only partially succeeded and they were not designed in the first instance to deal with such complex data. Therefore, there is a need for a new paradigm to deal with STBD. This paper reviews some methods of spiking neural networks (SNN) and argues that SNN are suitable for the creation of a unifying computational framework for learning and understanding of various STBD, such as EEG, fMRI, genetic, DTI, MEG, and NIRS, in their integration and interaction. One of the reasons is that SNN use the same computational principle that generates STBD, namely spiking information processing. This paper introduces a new SNN architecture, called NeuCube, for the creation of concrete models to map, learn and understand STBD. A NeuCube model is based on a 3D evolving SNN that is an approximate map of structural and functional areas of interest of the brain related to the modeling STBD. Gene information is included optionally in the form of gene regulatory networks (GRN) if this is relevant to the problem and the data. A NeuCube model learns from STBD and creates connections between clusters of neurons that manifest chains (trajectories) of neuronal activity. Once learning is applied, a NeuCube model can reproduce these trajectories, even if only part of the input STBD or the stimuli data is presented, thus acting as an associative memory. The NeuCube framework can be used not only to discover functional pathways from data, but also as a predictive system of brain activities, to predict and possibly, prevent certain events. Analysis of the internal structure of a model after training can reveal important spatio-temporal relationships 'hidden' in the data. NeuCube will allow the integration in one model of various brain data, information and knowledge, related to a single subject (personalized modeling) or to a population of subjects. The use of NeuCube for classification of STBD is illustrated in a case study problem of EEG data. NeuCube models result in a better accuracy of STBD classification than standard machine learning techniques. They are robust to noise (so typical in brain data) and facilitate a better interpretation of the results and understanding of the STBD and the brain conditions under which data was collected. Future directions for the use of SNN for STBD are discussed. © 2014 Elsevier Ltd.
McLeod L.,Auckland University of Technology |
MacDonell S.G.,Auckland University of Technology
ACM Computing Surveys | Year: 2011
Determining the factors that have an influence on software systems development and deployment project outcomes has been the focus of extensive and ongoing research for more than 30 years. We provide here a survey of the research literature that has addressed this topic in the period 1996-2006, with a particular focus on empirical analyses. On the basis of this survey we present a new classification framework that represents an abstracted and synthesized view of the types of factors that have been asserted as influencing project outcomes. © 2011 ACM.
Byard K.,Auckland University of Technology
Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment | Year: 2014
Fast decoding algorithms are described for a number of established coded aperture systems. The fast decoding algorithms for all these systems offer significant reductions in the number of calculations required when reconstructing images formed by a coded aperture system and hence require less computation time to produce the images. The algorithms may therefore be of use in applications that require fast image reconstruction, such as near real-time nuclear medicine and location of hazardous radioactive spillage. Experimental tests confirm the efficacy of the fast decoding techniques. © 2014 Elsevier B.V. All rights reserved.
Auckland University of Technology | Date: 2014-08-26
This invention involves use of temporal or spatio/spector-temporal data (SSTD) for early classification of outputs that are results of spatio-temporal patterns of data. Classification models are based on spiking neural networks (SNN) suitable to learn and classify SSTD. The invention may predict early events in many applications, i.e. engineering, bioinformatics, neuroinformatics, predicting response to treatment of neurological and brain disease, ecology, environment, medicine, and economics, among others. The invention involves a method and system for personalized modelling of SSTD and early prediction of events based on evolving spiking neural network reservoir architecture (eSNNr). The system includes a spike-time encoding module to encode continuous value input information into spike trains, a recurrent 3D SNNr and an eSSN as an output classification module.